Contact Dr Stephen King
- Tel: +44 (0) 1234 754642
- Email: S.P.King@cranfield.ac.uk
- ORCID
Areas of expertise
- Instrumentation, Sensors and Measurement Science
- Vehicle Health Management
Background
Steve is currently a part-time senior Lecturer in Advanced Analytics having recently retired from Rolls-Royce (April 2020) where he was an Engineering Associate Fellow and EHM Specialist working within the Rolls-Royce Digital organisation. During his 41-year career at Rolls-Royce he held positions within the Measurement Engineering group, Electronics and Measurement Techniques department, Strategic Research Centre, Business Process Improvement Centre, Controls Engineering and System Design Engineering. Prior to this he worked for Electronic Flow Meters where he was responsible for the test and commissioning of flow measurement systems in the oil and gas industry.
His main interests is in the use of data mining and advanced analytical techniques for asset health monitoring applications. Steve holds a degree in Mathematics and Computer Science and a PhD in the application of expert systems for vibration analysis. In addition to being a Chartered Engineer, he is a Fellow member of both the Institution of Engineering and Technology and the Institute of Mathematics and its Applications.
Publications
Articles In Journals
- Upadhyay A, Li J, King S & Addepalli S (2023) A deep-learning-based approach for aircraft engine defect detection, Machines, 11 (2) Article No. 192.
- Dangut MD, Jennions IK, King S & Skaf Z (2022) Application of deep reinforcement learning for extremely rare failure prediction in aircraft maintenance, Mechanical Systems and Signal Processing, 171 (May) Article No. 108873.
- Lee GK, Kasim H, Sirigina RP, How SS, King S & Hung TG (2022) Smart Robust Feature Selection (SoFt) for imbalanced and heterogeneous data, Knowledge-Based Systems, 236 (January) Article No. 107197.
- Skliros C, Ali F, King S & Jennions I (2022) Aircraft system-level diagnosis with emphasis on maintenance decisions, Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 236 (6) 1057-1077.
- Dangut MD, Jennions IK, King S & Skaf Z (2022) A rare failure detection model for aircraft predictive maintenance using a deep hybrid learning approach, Neural Computing and Applications, Available online 26 March 2022.
- Hullait H, Leslie D, Pavlidis N & King S (2020) Robust function-on-function regression, Technometrics, Available online 29 July 2020 (3).
- King S, Flint P & Sundaram S (2010) Handling sparse data problems in the context of monitoring multiple parameters in complex systems, Insight: Non-Destructive Testing & Condition Monitoring, 52 (8) 424-436.
- King S, Bannister P, Clifton D & Tarassenko L (2009) Probabilistic approach to the condition monitoring of aerospace engines, Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 223 (5) 533-541.
- Hayton P, Utete S, King DM, King SP, Anuzis P & Tarassenko L (2007) Static and dynamic novelty detection methods for jet engine health monitoring, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 365 (1851) 493-514.
- Nairac A, Townsend N, Carr R, King SP, Cowley P & Tarassenko L (1999) A system for the analysis of jet engine vibration data, Integrated Computer-Aided Engineering, 6 (1) 53-66.
- Allwood RJ, King SP & Pitts NJ (1996) The automatic interpretation of vibration data from gas turbines, The Aeronautical Journal, 100 (993) 99-107.
Conference Papers
- Wang CW, Fan IS & King S (2022) Failures mapping for aircraft electrical actuation system health management. In: 7th European Conference of the PHM Society (PHME 2022), Turin, 6-8 July 2022.
- Fu R, Harrison R, King S & Mills A (2016) Lean burn combustion monitoring strategy based on data modelling. In: 2016 IEEE Aerospace Conference, Big Sky, MT, 5-12 March 2016.
- King S, Erlund E, Clarkson R, Bird A & Da Col S (2012) Use of equipment health monitoring information for assessing potential exposure to volcanic events. In: 9th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies 2012 (CM 2012 AND MFPT 2012), London, 12-14 June 2012.
- King S, Adams R, Sundaram S & Dibsdale C (2011) Evaluation of compression techniques supporting off-line analysis of data from high-integrity assets. In: 8th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies 2011 (CM 2011/MFPT 2011), Cardiff, Wales, 20-22 June 2011.
- Sundaram S, Strachan I, Clifton D, Tarassenko L & King S (2009) Aircraft engine health monitoring using density modelling and extreme value statistics. In: 6th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies 2009, Dublin, 23-25 June 2009.
- King S, Ramos-Hernandez D, Moran J & Sundaram S (2009) Anomaly detection of combustor systems in support of unmanned air vehicle applications. In: 6th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies 2009, Dublin, 23-25 June 2009.
- Sundaram S, Strachan I, Clifton D, King S & Palmer J (2008) A data mining approach to reveal patterns in aircraft engine and operational data. In: Fifth International Conference on Condition Monitoring & Machinery Failure Prevention Technologies, Edinburgh, 15-18 July 2008.
- Clifton D, Bannister P, Taassenko L, Clifton L, Sundaram S & King S (2008) High Dimensional Visualisation for Novelty Detection. In: Fifth International Conference on Condition Monitoring & Machinery Failure Prevention Technologies, Edinburgh, 15-18 July 2008.
- Clifton DA, McGrogan N, Tarassenko L, King D, King S & Anuzis P (2008) Bayesian extreme value statistics for novelty detection in gas-turbine engines. In: 2008 IEEE Aerospace Conference, Big Sky, MT, 1-8 March 2008.
- King S, Anuzis P, King D, Tarassenko L, Utete S & Mcgrogan N (2006) A review of applications for advanced engine health monitoring in civil aircraft engines. In: 13th International Congress on Sound and Vibration (ICSV 2006), Vienna, 2-6 July 2006.
- King SP, King DM, Anuzis P, Astley K, Tarassenko L, Hayton P & Utete S (2002) The use of novelty detection techniques for monitoring high-integrity plant. In: 2002 IEEE International Conference on Control Applications, Glasgow, 18-20 September 2002.
- King S & Allwood R (1993) A blackboard approach to the interpretation of vibration characteristics in gas turbines. In: 6th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems.
- Allwood RJ, King SP & Pitts NJ (1992) Knowledge-based blackboard system to interpret graphical data from vibration tests of gas turbines. In: 17th International Conference on Applications of Artificial Intelligence in Engineering - AIENG/92, Waterloo.
- Ramos-Hernandez D & King S Pilot Evaluation of GRID technology for EHM applications. In: Fifth International Conference on Condition Monitoring & Machinery Failure Prevention Technologies, Edinburgh, 15-18 July 2008.
Books
- Hullait H, Leslie DS, Pavlidis NG & King S (2020) Robust functional regression for outlier detection. In: Advanced Analytics and Learning on Temporal Data, Springer.
- King S, Mills AR, Kardirkamanathan V & Clifton DA (2017) Equipment Health Monitoring in Complex Systems. Artech House.
- Tarassenko L, Clifton DA, Bannister PR, King S & King D (2009) Novelty detection. In: Encyclopedia of Structural Health Monitoring, Wiley, p. 653-689.
